Multi-Layered Temporal Entity Association Network for Supervision and Quality Evaluation in Online Education

The proliferation of online education has significantly enhanced teaching convenience; however, it has also led to a lack of effective student supervision. To address this issue, considering the vast amount of interrelated heterogeneous entity data with time information in a heterogeneous information space, this paper proposes a multi-layered time-centric model centered on entities and their association relationships. This temporal data model, namely the multi-layer temporal entity association network (MTE-Network), is designed to effectively capture the temporal information of heterogeneous entities and their associations. Furthermore, based on the proposed temporal data model, this study introduces a multi-modal fusion query model for entity search. This model enables users to search for any type of entity and their related entities within the heterogeneous information space. It supports entity search at the entity level, entity clustering level, and along the time axis, thereby fulfilling users’ diverse information needs for multi-modal fusion entity search. On top of the above models, this paper employs the Fuzzy Comprehensive Evaluation (FCE) method to obtain the evaluation grades and quantitative results of teachers’ classroom teaching quality. The fuzzy clustering algorithm is utilized to determine the main key condition attribute set. Subsequently, fuzzy data mining is applied to explore the rules between the evaluation levels and evaluation indexes of teachers’ classroom teaching quality within the evaluation database. The feasibility of this approach is verified through a practical application example of fuzzy data mining for classroom teaching quality.